Imaging a Black Hole using only Closure Quantities
In this tutorial, we will create a preliminary reconstruction of the 2017 M87 data on April 6 using closure-only imaging. This tutorial is a general introduction to closure-only imaging in Comrade. For an introduction to simultaneous image and instrument modeling, see Stokes I Simultaneous Image and Instrument Modeling
Introduction to Closure Imaging
The EHT is the highest-resolution telescope ever created. Its resolution is equivalent to roughly tracking a hockey puck on the moon when viewing it from the earth. However, the EHT is also a unique interferometer. For one, the data it produces is incredibly sparse. The array is formed from only eight geographic locations around the planet, each with its unique telescope. Additionally, the EHT observes at a much higher frequency than typical interferometers. As a result, it is often difficult to directly provide calibrated data since the source model can be complicated. This implies there can be large instrumental effects often called gains that can corrupt our signal. One way to deal with this is to fit quantities that are independent of gains. These are often called closure quantities. The types of closure quantities are briefly described in Introduction to the VLBI Imaging Problem.
In this tutorial, we will do closure-only modeling of M87 to produce preliminary images of M87.
To get started, we will load Comrade
using Comrade
using Pyehtim Activating project at `~/work/Comrade.jl/Comrade.jl/examples`For reproducibility we use a stable random number genreator
using StableRNGs
rng = StableRNG(123)StableRNGs.LehmerRNG(state=0x000000000000000000000000000000f7)Load the Data
To download the data visit https://doi.org/10.25739/g85n-f134 To load the eht-imaging obsdata object we do:
obs = ehtim.obsdata.load_uvfits(joinpath(dirname(pathof(Comrade)), "..", "examples", "SR1_M87_2017_096_lo_hops_netcal_StokesI.uvfits"))Python: <ehtim.obsdata.Obsdata object at 0x7fcb6e529570>Now we do some minor preprocessing:
- Scan average the data since the data have been preprocessed so that the gain phases are coherent.
- Add 1% systematic noise to deal with calibration issues that cause 1% non-closing errors.
obs = scan_average(obs).add_fractional_noise(0.015)Python: <ehtim.obsdata.Obsdata object at 0x7fcb6e52a9b0>Now, we extract our closure quantities from the EHT data set.
dlcamp, dcphase = extract_table(obs, LogClosureAmplitudes(;snrcut=3), ClosurePhases(;snrcut=3))(EHTObservation{Float64,Comrade.EHTLogClosureAmplitudeDatum{Float64}, ...}
source: M87
mjd: 57849
frequency: 2.27070703125e11
bandwidth: 1.856e9
stations: [:AA, :AP, :AZ, :JC, :LM, :PV, :SM]
nsamples: 128
, EHTObservation{Float64,Comrade.EHTClosurePhaseDatum{Float64}, ...}
source: M87
mjd: 57849
frequency: 2.27070703125e11
bandwidth: 1.856e9
stations: [:AA, :AP, :AZ, :JC, :LM, :PV, :SM]
nsamples: 152
)Build the Model/Posterior
For our model, we will be using an image model that consists of a raster of point sources, convolved with some pulse or kernel to make a ContinuousImage object with it Comrade's. generic image model.
function sky(θ, metadata)
(;fg, c, σimg) = θ
(;K, meanpr, grid, cache) = metadata
# Construct the image model we fix the flux to 0.6 Jy in this case
cp = meanpr .+ σimg.*c.params
rast = ((1-fg))*K(to_simplex(CenteredLR(), cp))
img = IntensityMap(rast, grid)
m = ContinuousImage(img, cache)
# Add a large-scale gaussian to deal with the over-resolved mas flux
g = modify(Gaussian(), Stretch(μas2rad(250.0), μas2rad(250.0)), Renormalize(fg))
return m + g
endsky (generic function with 1 method)Now, let's set up our image model. The EHT's nominal resolution is 20-25 μas. Additionally, the EHT is not very sensitive to a larger field of views; typically, 60-80 μas is enough to describe the compact flux of M87. Given this, we only need to use a small number of pixels to describe our image.
npix = 32
fovxy = μas2rad(150.0)7.27220521664304e-10Now, we can feed in the array information to form the cache
grid = imagepixels(fovxy, fovxy, npix, npix)
buffer = IntensityMap(zeros(npix,npix), grid)
cache = create_cache(NFFTAlg(dlcamp), buffer, BSplinePulse{3}())VLBISkyModels.NUFTCache{VLBISkyModels.ObservedNUFT{NFFTAlg{Float64, AbstractNFFTs.PrecomputeFlags, UInt32}, Matrix{Float64}}, NFFT.NFFTPlan{Float64, 2, 1}, Vector{ComplexF64}, BSplinePulse{3}, KeyedArray{Float64, 2, NamedDimsArray{(:X, :Y), Float64, 2, Matrix{Float64}}, GriddedKeys{(:X, :Y), Tuple{LinRange{Float64, Int64}, LinRange{Float64, Int64}}, ComradeBase.NoHeader, Float64}}}(VLBISkyModels.ObservedNUFT{NFFTAlg{Float64, AbstractNFFTs.PrecomputeFlags, UInt32}, Matrix{Float64}}(NFFTAlg{Float64, AbstractNFFTs.PrecomputeFlags, UInt32}(1, 4, 2.0, :kaiser_bessel, AbstractNFFTs.POLYNOMIAL, true, false, true, 0x00000000), [-4.405690154666661e9 787577.6145833326 … -5.999801315555549e9 -15551.297851562484; -4.523017159111106e9 -1.6838098888888871e6 … 3.059254300444441e9 118294.64453124987]), NFFTPlan with 274 sampling points for an input array of size(32, 32) and an output array of size(274,) with dims 1:2, ComplexF64[0.7014670924816703 - 0.519511453845776im, 0.9999999862106403 - 6.398624051637031e-5im, 0.6947985314160843 - 0.5233374146500747im, 0.9328845732113544 - 0.16336912604935844im, 0.9329421134238738 + 0.16331744240563614im, 0.6948519110447875 + 0.5233083088067162im, 0.7925669922789302 + 0.40478845348863063im, 0.9999999864389528 - 6.519719206334107e-5im, 0.9340555032742773 - 0.1520910238460368im, 0.6896791530194316 - 0.5261728587493705im … 0.8376666586657548 + 0.1784956664688869im, 0.9196690775195503 + 0.09173305645579018im, 0.9135584686897926 + 0.29199716871629755im, 0.9135610039654034 + 0.2919905620278203im, 0.8899638273254515 + 0.28719490128071723im, 0.9568100109264257 + 0.09814170760184636im, 0.8899622135777404 + 0.287201492222944im, 0.9925668796456906 + 0.0027765725149323534im, 0.8376650833595861 - 0.17848899851834446im, 0.9999999999247222 + 7.335331210317892e-6im], BSplinePulse{3}(), [0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0; … ; 0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0])Now we need to specify our image prior. For this work we will use a Gaussian Markov Random field prior
using VLBIImagePriors, Distributions, DistributionsADSince we are using a Gaussian Markov random field prior we need to first specify our mean image. For this work we will use a symmetric Gaussian with a FWHM of 50 μas
fwhmfac = 2*sqrt(2*log(2))
mpr = modify(Gaussian(), Stretch(μas2rad(50.0)./fwhmfac))
imgpr = intensitymap(mpr, grid)2-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓ X ∈ 32-element LinRange{Float64,...}
→ Y ∈ 32-element LinRange{Float64,...}
And data, 32×32 NamedDimsArray(::Matrix{Float64}, (:X, :Y)):
(-3.52247e-10) … (3.29522e-10) (3.52247e-10)
(-3.52247e-10) 6.37537e-8 1.32434e-7 6.37537e-8
(-3.29522e-10) 1.32434e-7 2.751e-7 1.32434e-7
(-3.06796e-10) 2.62014e-7 5.44273e-7 2.62014e-7
(-2.84071e-10) 4.93724e-7 1.0256e-6 4.93724e-7
(-2.61345e-10) 8.86092e-7 … 1.84065e-6 8.86092e-7
(-2.38619e-10) 1.51463e-6 3.14629e-6 1.51463e-6
⋮ ⋱ ⋮
(2.15894e-10) 2.46586e-6 5.12225e-6 2.46586e-6
(2.38619e-10) 1.51463e-6 3.14629e-6 1.51463e-6
(2.61345e-10) 8.86092e-7 … 1.84065e-6 8.86092e-7
(2.84071e-10) 4.93724e-7 1.0256e-6 4.93724e-7
(3.06796e-10) 2.62014e-7 5.44273e-7 2.62014e-7
(3.29522e-10) 1.32434e-7 2.751e-7 1.32434e-7
(3.52247e-10) 6.37537e-8 1.32434e-7 6.37537e-8Now since we are actually modeling our image on the simplex we need to ensure that our mean image has unit flux
imgpr ./= flux(imgpr)
meanpr = to_real(CenteredLR(), Comrade.baseimage(imgpr))
metadata = (;meanpr,K=CenterImage(imgpr), grid, cache)(meanpr = [-7.55422122563378 -6.823167558636962 … -6.823167558636962 -7.55422122563378; -6.823167558636962 -6.092113891640146 … -6.092113891640146 -6.823167558636962; … ; -6.823167558636962 -6.092113891640146 … -6.092113891640146 -6.823167558636962; -7.55422122563378 -6.823167558636962 … -6.823167558636962 -7.55422122563378], K = VLBIImagePriors.CenterImage{Matrix{Float64}, Tuple{Int64, Int64}}([0.9944957386363662 -0.005326704545454548 … 0.005326704545454572 0.005504261363636381; -0.005326704545454548 0.9948393969941349 … 0.005160603005865089 0.005326704545454565; … ; 0.005326704545454572 0.005160603005865089 … 0.9948393969941348 -0.005326704545454546; 0.005504261363636381 0.005326704545454565 … -0.005326704545454546 0.9944957386363638], (32, 32)), grid = GriddedKeys{(:X, :Y)}
X: LinRange{Float64}(-3.5224744018114725e-10, 3.5224744018114725e-10, 32)
Y: LinRange{Float64}(-3.5224744018114725e-10, 3.5224744018114725e-10, 32)
, cache = VLBISkyModels.NUFTCache{VLBISkyModels.ObservedNUFT{NFFTAlg{Float64, AbstractNFFTs.PrecomputeFlags, UInt32}, Matrix{Float64}}, NFFT.NFFTPlan{Float64, 2, 1}, Vector{ComplexF64}, BSplinePulse{3}, KeyedArray{Float64, 2, NamedDimsArray{(:X, :Y), Float64, 2, Matrix{Float64}}, GriddedKeys{(:X, :Y), Tuple{LinRange{Float64, Int64}, LinRange{Float64, Int64}}, ComradeBase.NoHeader, Float64}}}(VLBISkyModels.ObservedNUFT{NFFTAlg{Float64, AbstractNFFTs.PrecomputeFlags, UInt32}, Matrix{Float64}}(NFFTAlg{Float64, AbstractNFFTs.PrecomputeFlags, UInt32}(1, 4, 2.0, :kaiser_bessel, AbstractNFFTs.POLYNOMIAL, true, false, true, 0x00000000), [-4.405690154666661e9 787577.6145833326 … -5.999801315555549e9 -15551.297851562484; -4.523017159111106e9 -1.6838098888888871e6 … 3.059254300444441e9 118294.64453124987]), NFFTPlan with 274 sampling points for an input array of size(32, 32) and an output array of size(274,) with dims 1:2, ComplexF64[0.7014670924816703 - 0.519511453845776im, 0.9999999862106403 - 6.398624051637031e-5im, 0.6947985314160843 - 0.5233374146500747im, 0.9328845732113544 - 0.16336912604935844im, 0.9329421134238738 + 0.16331744240563614im, 0.6948519110447875 + 0.5233083088067162im, 0.7925669922789302 + 0.40478845348863063im, 0.9999999864389528 - 6.519719206334107e-5im, 0.9340555032742773 - 0.1520910238460368im, 0.6896791530194316 - 0.5261728587493705im … 0.8376666586657548 + 0.1784956664688869im, 0.9196690775195503 + 0.09173305645579018im, 0.9135584686897926 + 0.29199716871629755im, 0.9135610039654034 + 0.2919905620278203im, 0.8899638273254515 + 0.28719490128071723im, 0.9568100109264257 + 0.09814170760184636im, 0.8899622135777404 + 0.287201492222944im, 0.9925668796456906 + 0.0027765725149323534im, 0.8376650833595861 - 0.17848899851834446im, 0.9999999999247222 + 7.335331210317892e-6im], BSplinePulse{3}(), [0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0; … ; 0.0 0.0 … 0.0 0.0; 0.0 0.0 … 0.0 0.0]))In addition we want a reasonable guess for what the resolution of our image should be. For radio astronomy this is given by roughly the longest baseline in the image. To put this into pixel space we then divide by the pixel size.
beam = beamsize(dlcamp)
rat = (beam/(step(grid.X)))5.326336637737519To make the Gaussian Markov random field efficient we first precompute a bunch of quantities that allow us to scale things linearly with the number of image pixels. This drastically improves the usual N^3 scaling you get from usual Gaussian Processes.
crcache = MarkovRandomFieldCache(meanpr)VLBIImagePriors.MarkovRandomFieldCache{SparseArrays.SparseMatrixCSC{Float64, Int64}, LinearAlgebra.Diagonal{Float64, Vector{Float64}}, Matrix{Float64}}(sparse([1, 2, 32, 33, 993, 1, 2, 3, 34, 994 … 31, 991, 1022, 1023, 1024, 32, 992, 993, 1023, 1024], [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 31, 31, 31, 31, 31, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 34, 34, 34, 34, 34, 35, 35, 35, 35, 35, 36, 36, 36, 36, 36, 37, 37, 37, 37, 37, 38, 38, 38, 38, 38, 39, 39, 39, 39, 39, 40, 40, 40, 40, 40, 41, 41, 41, 41, 41, 42, 42, 42, 42, 42, 43, 43, 43, 43, 43, 44, 44, 44, 44, 44, 45, 45, 45, 45, 45, 46, 46, 46, 46, 46, 47, 47, 47, 47, 47, 48, 48, 48, 48, 48, 49, 49, 49, 49, 49, 50, 50, 50, 50, 50, 51, 51, 51, 51, 51, 52, 52, 52, 52, 52, 53, 53, 53, 53, 53, 54, 54, 54, 54, 54, 55, 55, 55, 55, 55, 56, 56, 56, 56, 56, 57, 57, 57, 57, 57, 58, 58, 58, 58, 58, 59, 59, 59, 59, 59, 60, 60, 60, 60, 60, 61, 61, 61, 61, 61, 62, 62, 62, 62, 62, 63, 63, 63, 63, 63, 64, 64, 64, 64, 64, 65, 65, 65, 65, 65, 66, 66, 66, 66, 66, 67, 67, 67, 67, 67, 68, 68, 68, 68, 68, 69, 69, 69, 69, 69, 70, 70, 70, 70, 70, 71, 71, 71, 71, 71, 72, 72, 72, 72, 72, 73, 73, 73, 73, 73, 74, 74, 74, 74, 74, 75, 75, 75, 75, 75, 76, 76, 76, 76, 76, 77, 77, 77, 77, 77, 78, 78, 78, 78, 78, 79, 79, 79, 79, 79, 80, 80, 80, 80, 80, 81, 81, 81, 81, 81, 82, 82, 82, 82, 82, 83, 83, 83, 83, 83, 84, 84, 84, 84, 84, 85, 85, 85, 85, 85, 86, 86, 86, 86, 86, 87, 87, 87, 87, 87, 88, 88, 88, 88, 88, 89, 89, 89, 89, 89, 90, 90, 90, 90, 90, 91, 91, 91, 91, 91, 92, 92, 92, 92, 92, 93, 93, 93, 93, 93, 94, 94, 94, 94, 94, 95, 95, 95, 95, 95, 96, 96, 96, 96, 96, 97, 97, 97, 97, 97, 98, 98, 98, 98, 98, 99, 99, 99, 99, 99, 100, 100, 100, 100, 100, 101, 101, 101, 101, 101, 102, 102, 102, 102, 102, 103, 103, 103, 103, 103, 104, 104, 104, 104, 104, 105, 105, 105, 105, 105, 106, 106, 106, 106, 106, 107, 107, 107, 107, 107, 108, 108, 108, 108, 108, 109, 109, 109, 109, 109, 110, 110, 110, 110, 110, 111, 111, 111, 111, 111, 112, 112, 112, 112, 112, 113, 113, 113, 113, 113, 114, 114, 114, 114, 114, 115, 115, 115, 115, 115, 116, 116, 116, 116, 116, 117, 117, 117, 117, 117, 118, 118, 118, 118, 118, 119, 119, 119, 119, 119, 120, 120, 120, 120, 120, 121, 121, 121, 121, 121, 122, 122, 122, 122, 122, 123, 123, 123, 123, 123, 124, 124, 124, 124, 124, 125, 125, 125, 125, 125, 126, 126, 126, 126, 126, 127, 127, 127, 127, 127, 128, 128, 128, 128, 128, 129, 129, 129, 129, 129, 130, 130, 130, 130, 130, 131, 131, 131, 131, 131, 132, 132, 132, 132, 132, 133, 133, 133, 133, 133, 134, 134, 134, 134, 134, 135, 135, 135, 135, 135, 136, 136, 136, 136, 136, 137, 137, 137, 137, 137, 138, 138, 138, 138, 138, 139, 139, 139, 139, 139, 140, 140, 140, 140, 140, 141, 141, 141, 141, 141, 142, 142, 142, 142, 142, 143, 143, 143, 143, 143, 144, 144, 144, 144, 144, 145, 145, 145, 145, 145, 146, 146, 146, 146, 146, 147, 147, 147, 147, 147, 148, 148, 148, 148, 148, 149, 149, 149, 149, 149, 150, 150, 150, 150, 150, 151, 151, 151, 151, 151, 152, 152, 152, 152, 152, 153, 153, 153, 153, 153, 154, 154, 154, 154, 154, 155, 155, 155, 155, 155, 156, 156, 156, 156, 156, 157, 157, 157, 157, 157, 158, 158, 158, 158, 158, 159, 159, 159, 159, 159, 160, 160, 160, 160, 160, 161, 161, 161, 161, 161, 162, 162, 162, 162, 162, 163, 163, 163, 163, 163, 164, 164, 164, 164, 164, 165, 165, 165, 165, 165, 166, 166, 166, 166, 166, 167, 167, 167, 167, 167, 168, 168, 168, 168, 168, 169, 169, 169, 169, 169, 170, 170, 170, 170, 170, 171, 171, 171, 171, 171, 172, 172, 172, 172, 172, 173, 173, 173, 173, 173, 174, 174, 174, 174, 174, 175, 175, 175, 175, 175, 176, 176, 176, 176, 176, 177, 177, 177, 177, 177, 178, 178, 178, 178, 178, 179, 179, 179, 179, 179, 180, 180, 180, 180, 180, 181, 181, 181, 181, 181, 182, 182, 182, 182, 182, 183, 183, 183, 183, 183, 184, 184, 184, 184, 184, 185, 185, 185, 185, 185, 186, 186, 186, 186, 186, 187, 187, 187, 187, 187, 188, 188, 188, 188, 188, 189, 189, 189, 189, 189, 190, 190, 190, 190, 190, 191, 191, 191, 191, 191, 192, 192, 192, 192, 192, 193, 193, 193, 193, 193, 194, 194, 194, 194, 194, 195, 195, 195, 195, 195, 196, 196, 196, 196, 196, 197, 197, 197, 197, 197, 198, 198, 198, 198, 198, 199, 199, 199, 199, 199, 200, 200, 200, 200, 200, 201, 201, 201, 201, 201, 202, 202, 202, 202, 202, 203, 203, 203, 203, 203, 204, 204, 204, 204, 204, 205, 205, 205, 205, 205, 206, 206, 206, 206, 206, 207, 207, 207, 207, 207, 208, 208, 208, 208, 208, 209, 209, 209, 209, 209, 210, 210, 210, 210, 210, 211, 211, 211, 211, 211, 212, 212, 212, 212, 212, 213, 213, 213, 213, 213, 214, 214, 214, 214, 214, 215, 215, 215, 215, 215, 216, 216, 216, 216, 216, 217, 217, 217, 217, 217, 218, 218, 218, 218, 218, 219, 219, 219, 219, 219, 220, 220, 220, 220, 220, 221, 221, 221, 221, 221, 222, 222, 222, 222, 222, 223, 223, 223, 223, 223, 224, 224, 224, 224, 224, 225, 225, 225, 225, 225, 226, 226, 226, 226, 226, 227, 227, 227, 227, 227, 228, 228, 228, 228, 228, 229, 229, 229, 229, 229, 230, 230, 230, 230, 230, 231, 231, 231, 231, 231, 232, 232, 232, 232, 232, 233, 233, 233, 233, 233, 234, 234, 234, 234, 234, 235, 235, 235, 235, 235, 236, 236, 236, 236, 236, 237, 237, 237, 237, 237, 238, 238, 238, 238, 238, 239, 239, 239, 239, 239, 240, 240, 240, 240, 240, 241, 241, 241, 241, 241, 242, 242, 242, 242, 242, 243, 243, 243, 243, 243, 244, 244, 244, 244, 244, 245, 245, 245, 245, 245, 246, 246, 246, 246, 246, 247, 247, 247, 247, 247, 248, 248, 248, 248, 248, 249, 249, 249, 249, 249, 250, 250, 250, 250, 250, 251, 251, 251, 251, 251, 252, 252, 252, 252, 252, 253, 253, 253, 253, 253, 254, 254, 254, 254, 254, 255, 255, 255, 255, 255, 256, 256, 256, 256, 256, 257, 257, 257, 257, 257, 258, 258, 258, 258, 258, 259, 259, 259, 259, 259, 260, 260, 260, 260, 260, 261, 261, 261, 261, 261, 262, 262, 262, 262, 262, 263, 263, 263, 263, 263, 264, 264, 264, 264, 264, 265, 265, 265, 265, 265, 266, 266, 266, 266, 266, 267, 267, 267, 267, 267, 268, 268, 268, 268, 268, 269, 269, 269, 269, 269, 270, 270, 270, 270, 270, 271, 271, 271, 271, 271, 272, 272, 272, 272, 272, 273, 273, 273, 273, 273, 274, 274, 274, 274, 274, 275, 275, 275, 275, 275, 276, 276, 276, 276, 276, 277, 277, 277, 277, 277, 278, 278, 278, 278, 278, 279, 279, 279, 279, 279, 280, 280, 280, 280, 280, 281, 281, 281, 281, 281, 282, 282, 282, 282, 282, 283, 283, 283, 283, 283, 284, 284, 284, 284, 284, 285, 285, 285, 285, 285, 286, 286, 286, 286, 286, 287, 287, 287, 287, 287, 288, 288, 288, 288, 288, 289, 289, 289, 289, 289, 290, 290, 290, 290, 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1009, 1009, 1009, 1009, 1009, 1010, 1010, 1010, 1010, 1010, 1011, 1011, 1011, 1011, 1011, 1012, 1012, 1012, 1012, 1012, 1013, 1013, 1013, 1013, 1013, 1014, 1014, 1014, 1014, 1014, 1015, 1015, 1015, 1015, 1015, 1016, 1016, 1016, 1016, 1016, 1017, 1017, 1017, 1017, 1017, 1018, 1018, 1018, 1018, 1018, 1019, 1019, 1019, 1019, 1019, 1020, 1020, 1020, 1020, 1020, 1021, 1021, 1021, 1021, 1021, 1022, 1022, 1022, 1022, 1022, 1023, 1023, 1023, 1023, 1023, 1024, 1024, 1024, 1024, 1024], [4.0, -1.0, -1.0, -1.0, -1.0, -1.0, 4.0, -1.0, -1.0, -1.0 … -1.0, -1.0, -1.0, 4.0, -1.0, -1.0, -1.0, -1.0, -1.0, 4.0], 1024, 1024), [1.0 0.0 … 0.0 0.0; 0.0 1.0 … 0.0 0.0; … ; 0.0 0.0 … 1.0 0.0; 0.0 0.0 … 0.0 1.0], [0.0 0.03842943919353914 … 0.15224093497742697 0.03842943919353936; 0.03842943919353914 0.07685887838707828 … 0.1906703741709661 0.0768588783870785; … ; 0.15224093497742697 0.1906703741709661 … 0.30448186995485393 0.19067037417096633; 0.03842943919353914 0.07685887838707828 … 0.1906703741709661 0.0768588783870785])One of the benefits of the Bayesian approach is that we can fit for the hyperparameters of our prior/regularizers unlike traditional RML appraoches. To construct this heirarchical prior we will first make a map that takes in our regularizer hyperparameters and returns the image prior given those hyperparameters.
fmap = let crcache=crcache
x->GaussMarkovRandomField(x, 1.0, crcache)
end#1 (generic function with 1 method)Now we can finally form our image prior. For this we use a heirarchical prior where the correlation length is given by a inverse gamma prior to prevent overfitting. Gaussian Markov random fields are extremly flexible models. To prevent overfitting it is common to use priors that penalize complexity. Therefore, we want to use priors that enforce similarity to our mean image, and prefer smoothness.
cprior = HierarchicalPrior(fmap, InverseGamma(1.0, -log(0.01*rat)))
prior = NamedDist(c = cprior, σimg = truncated(Normal(0.0, 0.1); lower = 0.0), fg=Uniform(0.0, 1.0))
lklhd = RadioLikelihood(sky, dlcamp, dcphase;
skymeta = metadata)
post = Posterior(lklhd, prior)Posterior{RadioLikelihood{Comrade.ModelMetadata{typeof(Main.sky), NamedTuple{(:meanpr, :K, :grid, :cache), Tuple{Matrix{Float64}, VLBIImagePriors.CenterImage{Matrix{Float64}, Tuple{Int64, Int64}}, GriddedKeys{(:X, :Y), Tuple{LinRange{Float64, Int64}, LinRange{Float64, Int64}}, ComradeBase.NoHeader, Float64}, VLBISkyModels.NUFTCache{VLBISkyModels.ObservedNUFT{NFFTAlg{Float64, AbstractNFFTs.PrecomputeFlags, UInt32}, Matrix{Float64}}, NFFT.NFFTPlan{Float64, 2, 1}, Vector{ComplexF64}, BSplinePulse{3}, KeyedArray{Float64, 2, NamedDimsArray{(:X, :Y), Float64, 2, Matrix{Float64}}, GriddedKeys{(:X, :Y), Tuple{LinRange{Float64, Int64}, LinRange{Float64, Int64}}, ComradeBase.NoHeader, Float64}}}}}}, Nothing, Tuple{Comrade.EHTObservation{Float64, Comrade.EHTLogClosureAmplitudeDatum{Float64}, StructArrays.StructVector{Comrade.EHTLogClosureAmplitudeDatum{Float64}, NamedTuple{(:measurement, :error, :U1, :V1, :U2, :V2, :U3, :V3, :U4, :V4, :T, :F, :quadrangle), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{NTuple{4, Symbol}}}}, Int64}, Comrade.ClosureConfig{Comrade.EHTObservation{Float64, Comrade.EHTVisibilityDatum{Float64}, StructArrays.StructVector{Comrade.EHTVisibilityDatum{Float64}, NamedTuple{(:measurement, :error, :U, :V, :T, :F, :baseline), Tuple{Vector{ComplexF64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}}}, Int64}, Comrade.EHTArrayConfiguration{Float64, TypedTables.Table{NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Symbol, Vararg{Float64, 8}}}, 1, NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Vector{Symbol}, Vararg{Vector{Float64}, 8}}}}, TypedTables.Table{NamedTuple{(:start, :stop), Tuple{Float64, Float64}}, 1, NamedTuple{(:start, :stop), Tuple{Vector{Float64}, Vector{Float64}}}}, StructArrays.StructVector{Comrade.ArrayBaselineDatum, NamedTuple{(:U, :V, :T, :F, :baseline, :error, :elevation, :parallactic), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}, Vector{Float64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}}}, Int64}}, Int64}, SparseArrays.SparseMatrixCSC{Float64, Int64}}, Int64}, Comrade.EHTObservation{Float64, Comrade.EHTClosurePhaseDatum{Float64}, StructArrays.StructVector{Comrade.EHTClosurePhaseDatum{Float64}, NamedTuple{(:measurement, :error, :U1, :V1, :U2, :V2, :U3, :V3, :T, :F, :triangle), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol, Symbol}}}}, Int64}, Comrade.ClosureConfig{Comrade.EHTObservation{Float64, Comrade.EHTVisibilityDatum{Float64}, StructArrays.StructVector{Comrade.EHTVisibilityDatum{Float64}, NamedTuple{(:measurement, :error, :U, :V, :T, :F, :baseline), Tuple{Vector{ComplexF64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}}}, Int64}, Comrade.EHTArrayConfiguration{Float64, TypedTables.Table{NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Symbol, Vararg{Float64, 8}}}, 1, NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Vector{Symbol}, Vararg{Vector{Float64}, 8}}}}, TypedTables.Table{NamedTuple{(:start, :stop), Tuple{Float64, Float64}}, 1, NamedTuple{(:start, :stop), Tuple{Vector{Float64}, Vector{Float64}}}}, StructArrays.StructVector{Comrade.ArrayBaselineDatum, NamedTuple{(:U, :V, :T, :F, :baseline, :error, :elevation, :parallactic), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}, Vector{Float64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}}}, Int64}}, Int64}, SparseArrays.SparseMatrixCSC{Float64, Int64}}, Int64}}, Tuple{Comrade.ConditionedLikelihood{Comrade.var"#34#35"{Float64, Base.Fix2{typeof(logclosure_amplitudes), Comrade.ClosureConfig{Comrade.EHTObservation{Float64, Comrade.EHTVisibilityDatum{Float64}, StructArrays.StructVector{Comrade.EHTVisibilityDatum{Float64}, NamedTuple{(:measurement, :error, :U, :V, :T, :F, :baseline), Tuple{Vector{ComplexF64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}}}, Int64}, Comrade.EHTArrayConfiguration{Float64, TypedTables.Table{NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Symbol, Vararg{Float64, 8}}}, 1, NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Vector{Symbol}, Vararg{Vector{Float64}, 8}}}}, TypedTables.Table{NamedTuple{(:start, :stop), Tuple{Float64, Float64}}, 1, NamedTuple{(:start, :stop), Tuple{Vector{Float64}, Vector{Float64}}}}, StructArrays.StructVector{Comrade.ArrayBaselineDatum, NamedTuple{(:U, :V, :T, :F, :baseline, :error, :elevation, :parallactic), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}, Vector{Float64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}}}, Int64}}, Int64}, SparseArrays.SparseMatrixCSC{Float64, Int64}}}, VLBILikelihoods.CholeskyFactor{Float64, SparseArrays.SparseMatrixCSC{Float64, Int64}, SparseArrays.CHOLMOD.Factor{Float64}}}, Vector{Float64}}, Comrade.ConditionedLikelihood{Comrade.var"#36#37"{Float64, Base.Fix2{typeof(closure_phases), Comrade.ClosureConfig{Comrade.EHTObservation{Float64, Comrade.EHTVisibilityDatum{Float64}, StructArrays.StructVector{Comrade.EHTVisibilityDatum{Float64}, NamedTuple{(:measurement, :error, :U, :V, :T, :F, :baseline), Tuple{Vector{ComplexF64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}}}, Int64}, Comrade.EHTArrayConfiguration{Float64, TypedTables.Table{NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Symbol, Vararg{Float64, 8}}}, 1, NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Vector{Symbol}, Vararg{Vector{Float64}, 8}}}}, TypedTables.Table{NamedTuple{(:start, :stop), Tuple{Float64, Float64}}, 1, NamedTuple{(:start, :stop), Tuple{Vector{Float64}, Vector{Float64}}}}, StructArrays.StructVector{Comrade.ArrayBaselineDatum, NamedTuple{(:U, :V, :T, :F, :baseline, :error, :elevation, :parallactic), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}, Vector{Float64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}}}, Int64}}, Int64}, SparseArrays.SparseMatrixCSC{Float64, Int64}}}, VLBILikelihoods.CholeskyFactor{Float64, SparseArrays.SparseMatrixCSC{Float64, Int64}, SparseArrays.CHOLMOD.Factor{Float64}}}, Vector{Float64}}}, Comrade.ClosureConfig{Comrade.EHTObservation{Float64, Comrade.EHTVisibilityDatum{Float64}, StructArrays.StructVector{Comrade.EHTVisibilityDatum{Float64}, NamedTuple{(:measurement, :error, :U, :V, :T, :F, :baseline), Tuple{Vector{ComplexF64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}}}, Int64}, Comrade.EHTArrayConfiguration{Float64, TypedTables.Table{NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Symbol, Vararg{Float64, 8}}}, 1, NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Vector{Symbol}, Vararg{Vector{Float64}, 8}}}}, TypedTables.Table{NamedTuple{(:start, :stop), Tuple{Float64, Float64}}, 1, NamedTuple{(:start, :stop), Tuple{Vector{Float64}, Vector{Float64}}}}, StructArrays.StructVector{Comrade.ArrayBaselineDatum, NamedTuple{(:U, :V, :T, :F, :baseline, :error, :elevation, :parallactic), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}, Vector{Float64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}}}, Int64}}, Int64}, SparseArrays.SparseMatrixCSC{Float64, Int64}}, NamedTuple{(:U, :V, :T, :F), NTuple{4, Vector{Float64}}}}, VLBIImagePriors.NamedDist{(:c, :σimg, :fg), Tuple{VLBIImagePriors.HierarchicalPrior{Main.var"#1#2"{VLBIImagePriors.MarkovRandomFieldCache{SparseArrays.SparseMatrixCSC{Float64, Int64}, LinearAlgebra.Diagonal{Float64, Vector{Float64}}, Matrix{Float64}}}, Distributions.InverseGamma{Float64}}, Distributions.Truncated{Distributions.Normal{Float64}, Distributions.Continuous, Float64, Float64, Nothing}, Distributions.Uniform{Float64}}}}(RadioLikelihood
Number of data products: 2
, VLBIImagePriors.NamedDist{(:c, :σimg, :fg), Tuple{VLBIImagePriors.HierarchicalPrior{Main.var"#1#2"{VLBIImagePriors.MarkovRandomFieldCache{SparseArrays.SparseMatrixCSC{Float64, Int64}, LinearAlgebra.Diagonal{Float64, Vector{Float64}}, Matrix{Float64}}}, Distributions.InverseGamma{Float64}}, Distributions.Truncated{Distributions.Normal{Float64}, Distributions.Continuous, Float64, Float64, Nothing}, Distributions.Uniform{Float64}}}(
dists: (VLBIImagePriors.HierarchicalPrior{Main.var"#1#2"{VLBIImagePriors.MarkovRandomFieldCache{SparseArrays.SparseMatrixCSC{Float64, Int64}, LinearAlgebra.Diagonal{Float64, Vector{Float64}}, Matrix{Float64}}}, Distributions.InverseGamma{Float64}}(
priormap: #1
hyperprior: Distributions.InverseGamma{Float64}(
invd: Distributions.Gamma{Float64}(α=1.0, θ=0.3410052124381751)
θ: 2.932506494109095
)
)
, Truncated(Distributions.Normal{Float64}(μ=0.0, σ=0.1); lower=0.0), Distributions.Uniform{Float64}(a=0.0, b=1.0))
)
)Reconstructing the Image
To sample from this posterior, it is convenient to first move from our constrained parameter space to an unconstrained one (i.e., the support of the transformed posterior is (-∞, ∞)). This is done using the asflat function.
tpost = asflat(post)Comrade.TransformedPosterior{Posterior{RadioLikelihood{Comrade.ModelMetadata{typeof(Main.sky), NamedTuple{(:meanpr, :K, :grid, :cache), Tuple{Matrix{Float64}, VLBIImagePriors.CenterImage{Matrix{Float64}, Tuple{Int64, Int64}}, GriddedKeys{(:X, :Y), Tuple{LinRange{Float64, Int64}, LinRange{Float64, Int64}}, ComradeBase.NoHeader, Float64}, VLBISkyModels.NUFTCache{VLBISkyModels.ObservedNUFT{NFFTAlg{Float64, AbstractNFFTs.PrecomputeFlags, UInt32}, Matrix{Float64}}, NFFT.NFFTPlan{Float64, 2, 1}, Vector{ComplexF64}, BSplinePulse{3}, KeyedArray{Float64, 2, NamedDimsArray{(:X, :Y), Float64, 2, Matrix{Float64}}, GriddedKeys{(:X, :Y), Tuple{LinRange{Float64, Int64}, LinRange{Float64, Int64}}, ComradeBase.NoHeader, Float64}}}}}}, Nothing, Tuple{Comrade.EHTObservation{Float64, Comrade.EHTLogClosureAmplitudeDatum{Float64}, StructArrays.StructVector{Comrade.EHTLogClosureAmplitudeDatum{Float64}, NamedTuple{(:measurement, :error, :U1, :V1, :U2, :V2, :U3, :V3, :U4, :V4, :T, :F, :quadrangle), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{NTuple{4, Symbol}}}}, Int64}, Comrade.ClosureConfig{Comrade.EHTObservation{Float64, Comrade.EHTVisibilityDatum{Float64}, StructArrays.StructVector{Comrade.EHTVisibilityDatum{Float64}, NamedTuple{(:measurement, :error, :U, :V, :T, :F, :baseline), Tuple{Vector{ComplexF64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}}}, Int64}, Comrade.EHTArrayConfiguration{Float64, TypedTables.Table{NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Symbol, Vararg{Float64, 8}}}, 1, NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Vector{Symbol}, Vararg{Vector{Float64}, 8}}}}, TypedTables.Table{NamedTuple{(:start, :stop), Tuple{Float64, Float64}}, 1, NamedTuple{(:start, :stop), Tuple{Vector{Float64}, Vector{Float64}}}}, StructArrays.StructVector{Comrade.ArrayBaselineDatum, NamedTuple{(:U, :V, :T, :F, :baseline, :error, :elevation, :parallactic), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}, Vector{Float64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}}}, Int64}}, Int64}, SparseArrays.SparseMatrixCSC{Float64, Int64}}, Int64}, Comrade.EHTObservation{Float64, Comrade.EHTClosurePhaseDatum{Float64}, StructArrays.StructVector{Comrade.EHTClosurePhaseDatum{Float64}, NamedTuple{(:measurement, :error, :U1, :V1, :U2, :V2, :U3, :V3, :T, :F, :triangle), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol, Symbol}}}}, Int64}, Comrade.ClosureConfig{Comrade.EHTObservation{Float64, Comrade.EHTVisibilityDatum{Float64}, StructArrays.StructVector{Comrade.EHTVisibilityDatum{Float64}, NamedTuple{(:measurement, :error, :U, :V, :T, :F, :baseline), Tuple{Vector{ComplexF64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}}}, Int64}, Comrade.EHTArrayConfiguration{Float64, TypedTables.Table{NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Symbol, Vararg{Float64, 8}}}, 1, NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Vector{Symbol}, Vararg{Vector{Float64}, 8}}}}, TypedTables.Table{NamedTuple{(:start, :stop), Tuple{Float64, Float64}}, 1, NamedTuple{(:start, :stop), Tuple{Vector{Float64}, Vector{Float64}}}}, StructArrays.StructVector{Comrade.ArrayBaselineDatum, NamedTuple{(:U, :V, :T, :F, :baseline, :error, :elevation, :parallactic), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}, Vector{Float64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}}}, Int64}}, Int64}, SparseArrays.SparseMatrixCSC{Float64, Int64}}, Int64}}, Tuple{Comrade.ConditionedLikelihood{Comrade.var"#34#35"{Float64, Base.Fix2{typeof(logclosure_amplitudes), Comrade.ClosureConfig{Comrade.EHTObservation{Float64, Comrade.EHTVisibilityDatum{Float64}, StructArrays.StructVector{Comrade.EHTVisibilityDatum{Float64}, NamedTuple{(:measurement, :error, :U, :V, :T, :F, :baseline), Tuple{Vector{ComplexF64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}}}, Int64}, Comrade.EHTArrayConfiguration{Float64, TypedTables.Table{NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Symbol, Vararg{Float64, 8}}}, 1, NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Vector{Symbol}, Vararg{Vector{Float64}, 8}}}}, TypedTables.Table{NamedTuple{(:start, :stop), Tuple{Float64, Float64}}, 1, NamedTuple{(:start, :stop), Tuple{Vector{Float64}, Vector{Float64}}}}, StructArrays.StructVector{Comrade.ArrayBaselineDatum, NamedTuple{(:U, :V, :T, :F, :baseline, :error, :elevation, :parallactic), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}, Vector{Float64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}}}, Int64}}, Int64}, SparseArrays.SparseMatrixCSC{Float64, Int64}}}, 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TransformVariables.ScaledShiftedLogistic{Float64}}}}}(Posterior{RadioLikelihood{Comrade.ModelMetadata{typeof(Main.sky), NamedTuple{(:meanpr, :K, :grid, :cache), Tuple{Matrix{Float64}, VLBIImagePriors.CenterImage{Matrix{Float64}, Tuple{Int64, Int64}}, GriddedKeys{(:X, :Y), Tuple{LinRange{Float64, Int64}, LinRange{Float64, Int64}}, ComradeBase.NoHeader, Float64}, VLBISkyModels.NUFTCache{VLBISkyModels.ObservedNUFT{NFFTAlg{Float64, AbstractNFFTs.PrecomputeFlags, UInt32}, Matrix{Float64}}, NFFT.NFFTPlan{Float64, 2, 1}, Vector{ComplexF64}, BSplinePulse{3}, KeyedArray{Float64, 2, NamedDimsArray{(:X, :Y), Float64, 2, Matrix{Float64}}, GriddedKeys{(:X, :Y), Tuple{LinRange{Float64, Int64}, LinRange{Float64, Int64}}, ComradeBase.NoHeader, Float64}}}}}}, Nothing, Tuple{Comrade.EHTObservation{Float64, Comrade.EHTLogClosureAmplitudeDatum{Float64}, StructArrays.StructVector{Comrade.EHTLogClosureAmplitudeDatum{Float64}, NamedTuple{(:measurement, :error, :U1, :V1, :U2, :V2, :U3, :V3, :U4, :V4, :T, :F, :quadrangle), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{NTuple{4, Symbol}}}}, Int64}, Comrade.ClosureConfig{Comrade.EHTObservation{Float64, Comrade.EHTVisibilityDatum{Float64}, StructArrays.StructVector{Comrade.EHTVisibilityDatum{Float64}, NamedTuple{(:measurement, :error, :U, :V, :T, :F, :baseline), Tuple{Vector{ComplexF64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}}}, Int64}, Comrade.EHTArrayConfiguration{Float64, TypedTables.Table{NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Symbol, Vararg{Float64, 8}}}, 1, NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Vector{Symbol}, Vararg{Vector{Float64}, 8}}}}, TypedTables.Table{NamedTuple{(:start, :stop), Tuple{Float64, Float64}}, 1, NamedTuple{(:start, :stop), Tuple{Vector{Float64}, Vector{Float64}}}}, StructArrays.StructVector{Comrade.ArrayBaselineDatum, NamedTuple{(:U, :V, :T, :F, :baseline, :error, :elevation, :parallactic), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}, Vector{Float64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}}}, Int64}}, Int64}, SparseArrays.SparseMatrixCSC{Float64, Int64}}, Int64}, Comrade.EHTObservation{Float64, Comrade.EHTClosurePhaseDatum{Float64}, StructArrays.StructVector{Comrade.EHTClosurePhaseDatum{Float64}, NamedTuple{(:measurement, :error, :U1, :V1, :U2, :V2, :U3, :V3, :T, :F, :triangle), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol, Symbol}}}}, Int64}, Comrade.ClosureConfig{Comrade.EHTObservation{Float64, Comrade.EHTVisibilityDatum{Float64}, StructArrays.StructVector{Comrade.EHTVisibilityDatum{Float64}, NamedTuple{(:measurement, :error, :U, :V, :T, :F, :baseline), Tuple{Vector{ComplexF64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}}}, Int64}, Comrade.EHTArrayConfiguration{Float64, TypedTables.Table{NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Symbol, Vararg{Float64, 8}}}, 1, NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Vector{Symbol}, Vararg{Vector{Float64}, 8}}}}, TypedTables.Table{NamedTuple{(:start, :stop), Tuple{Float64, Float64}}, 1, NamedTuple{(:start, :stop), Tuple{Vector{Float64}, Vector{Float64}}}}, StructArrays.StructVector{Comrade.ArrayBaselineDatum, NamedTuple{(:U, :V, :T, :F, :baseline, :error, :elevation, :parallactic), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}, Vector{Float64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}}}, Int64}}, Int64}, SparseArrays.SparseMatrixCSC{Float64, Int64}}, Int64}}, Tuple{Comrade.ConditionedLikelihood{Comrade.var"#34#35"{Float64, Base.Fix2{typeof(logclosure_amplitudes), Comrade.ClosureConfig{Comrade.EHTObservation{Float64, Comrade.EHTVisibilityDatum{Float64}, StructArrays.StructVector{Comrade.EHTVisibilityDatum{Float64}, NamedTuple{(:measurement, :error, :U, :V, :T, :F, :baseline), Tuple{Vector{ComplexF64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}}}, Int64}, Comrade.EHTArrayConfiguration{Float64, TypedTables.Table{NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Symbol, Vararg{Float64, 8}}}, 1, NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Vector{Symbol}, Vararg{Vector{Float64}, 8}}}}, TypedTables.Table{NamedTuple{(:start, :stop), Tuple{Float64, Float64}}, 1, NamedTuple{(:start, :stop), Tuple{Vector{Float64}, Vector{Float64}}}}, StructArrays.StructVector{Comrade.ArrayBaselineDatum, NamedTuple{(:U, :V, :T, :F, :baseline, :error, :elevation, :parallactic), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}, Vector{Float64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}}}, Int64}}, Int64}, SparseArrays.SparseMatrixCSC{Float64, Int64}}}, VLBILikelihoods.CholeskyFactor{Float64, SparseArrays.SparseMatrixCSC{Float64, Int64}, SparseArrays.CHOLMOD.Factor{Float64}}}, Vector{Float64}}, Comrade.ConditionedLikelihood{Comrade.var"#36#37"{Float64, Base.Fix2{typeof(closure_phases), Comrade.ClosureConfig{Comrade.EHTObservation{Float64, Comrade.EHTVisibilityDatum{Float64}, StructArrays.StructVector{Comrade.EHTVisibilityDatum{Float64}, NamedTuple{(:measurement, :error, :U, :V, :T, :F, :baseline), Tuple{Vector{ComplexF64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}}}, Int64}, Comrade.EHTArrayConfiguration{Float64, TypedTables.Table{NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Symbol, Vararg{Float64, 8}}}, 1, NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Vector{Symbol}, Vararg{Vector{Float64}, 8}}}}, TypedTables.Table{NamedTuple{(:start, :stop), Tuple{Float64, 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Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}}}, Int64}, Comrade.EHTArrayConfiguration{Float64, TypedTables.Table{NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Symbol, Vararg{Float64, 8}}}, 1, NamedTuple{(:sites, :X, :Y, :Z, :SEFD1, :SEFD2, :fr_parallactic, :fr_elevation, :fr_offset), Tuple{Vector{Symbol}, Vararg{Vector{Float64}, 8}}}}, TypedTables.Table{NamedTuple{(:start, :stop), Tuple{Float64, Float64}}, 1, NamedTuple{(:start, :stop), Tuple{Vector{Float64}, Vector{Float64}}}}, StructArrays.StructVector{Comrade.ArrayBaselineDatum, NamedTuple{(:U, :V, :T, :F, :baseline, :error, :elevation, :parallactic), Tuple{Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Float64}, Vector{Tuple{Symbol, Symbol}}, Vector{Float64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}, StructArrays.StructVector{Tuple{Float64, Float64}, Tuple{Vector{Float64}, Vector{Float64}}, Int64}}}, Int64}}, Int64}, SparseArrays.SparseMatrixCSC{Float64, Int64}}, NamedTuple{(:U, :V, :T, :F), NTuple{4, Vector{Float64}}}}, VLBIImagePriors.NamedDist{(:c, :σimg, :fg), Tuple{VLBIImagePriors.HierarchicalPrior{Main.var"#1#2"{VLBIImagePriors.MarkovRandomFieldCache{SparseArrays.SparseMatrixCSC{Float64, Int64}, LinearAlgebra.Diagonal{Float64, Vector{Float64}}, Matrix{Float64}}}, Distributions.InverseGamma{Float64}}, Distributions.Truncated{Distributions.Normal{Float64}, Distributions.Continuous, Float64, Float64, Nothing}, Distributions.Uniform{Float64}}}}(RadioLikelihood
Number of data products: 2
, VLBIImagePriors.NamedDist{(:c, :σimg, :fg), Tuple{VLBIImagePriors.HierarchicalPrior{Main.var"#1#2"{VLBIImagePriors.MarkovRandomFieldCache{SparseArrays.SparseMatrixCSC{Float64, Int64}, LinearAlgebra.Diagonal{Float64, Vector{Float64}}, Matrix{Float64}}}, Distributions.InverseGamma{Float64}}, Distributions.Truncated{Distributions.Normal{Float64}, Distributions.Continuous, Float64, Float64, Nothing}, Distributions.Uniform{Float64}}}(
dists: (VLBIImagePriors.HierarchicalPrior{Main.var"#1#2"{VLBIImagePriors.MarkovRandomFieldCache{SparseArrays.SparseMatrixCSC{Float64, Int64}, LinearAlgebra.Diagonal{Float64, Vector{Float64}}, Matrix{Float64}}}, Distributions.InverseGamma{Float64}}(
priormap: #1
hyperprior: Distributions.InverseGamma{Float64}(
invd: Distributions.Gamma{Float64}(α=1.0, θ=0.3410052124381751)
θ: 2.932506494109095
)
)
, Truncated(Distributions.Normal{Float64}(μ=0.0, σ=0.1); lower=0.0), Distributions.Uniform{Float64}(a=0.0, b=1.0))
)
), TransformVariables.TransformTuple{NamedTuple{(:c, :σimg, :fg), Tuple{TransformVariables.TransformTuple{NamedTuple{(:params, :hyperparams), Tuple{TransformVariables.ArrayTransformation{TransformVariables.Identity, 2}, TransformVariables.ShiftedExp{true, Float64}}}}, TransformVariables.ShiftedExp{true, Float64}, TransformVariables.ScaledShiftedLogistic{Float64}}}}((c = TransformVariables.TransformTuple{NamedTuple{(:params, :hyperparams), Tuple{TransformVariables.ArrayTransformation{TransformVariables.Identity, 2}, TransformVariables.ShiftedExp{true, Float64}}}}((params = TransformVariables.ArrayTransformation{TransformVariables.Identity, 2}(asℝ, (32, 32)), hyperparams = as(Real, 0.0, ∞)), 1025), σimg = as(Real, 0.0, ∞), fg = as(Real, 0.0, 1.0)), 1027))We can now also find the dimension of our posterior or the number of parameters we will sample.
This can often be different from what you would expect. This is especially true when using angular variables, where we often artificially increase the dimension of the parameter space to make sampling easier.
ndim = dimension(tpost)1027Now we optimize using LBFGS
using ComradeOptimization
using OptimizationOptimJL
using Zygote
f = OptimizationFunction(tpost, Optimization.AutoZygote())
prob = Optimization.OptimizationProblem(f, prior_sample(rng, tpost), nothing)
sol = solve(prob, LBFGS(); maxiters=5_00);Before we analyze our solution we first need to transform back to parameter space.
xopt = transform(tpost, sol)(c = (params = [-0.7697277078406277 -0.7993005840689281 … -0.7194335792404548 -0.7382089247013944; -0.7824705275175383 -0.806143529083471 … -0.7327469531818241 -0.7528884161482982; … ; -0.7369981481411635 -0.7689412040294166 … -0.692583309004249 -0.7109910568959764; -0.7550134622193175 -0.7847301622170063 … -0.7053813339571418 -0.7243747135090771], hyperparams = 14.483859437464675), σimg = 0.7473961476653416, fg = 0.039558616303718507)First we will evaluate our fit by plotting the residuals
using Plots
residual(skymodel(post, xopt), dlcamp, ylabel="Log Closure Amplitude Res.")
and now closure phases
residual(skymodel(post, xopt), dcphase, ylabel="|Closure Phase Res.|")
Now let's plot the MAP estimate.
import WGLMakie as CM
img = intensitymap(skymodel(post, xopt), μas2rad(150.0), μas2rad(150.0), 100, 100)
CM.image(img, axis=(xreversed=true, aspect=1, title="MAP Image"), colormap=:afmhot, figure=(;resolution=(400, 400),))